gimlidc / igre

Information gain with registration - multimodal image processing tool

IGRE project

IGRE means Information Gain with REgistration. It is a scientific project originally focused on ART, but with many overlaps to different areas with multimodal datasets

Smart CLI

Igre contains several tools which can be useful for data manipulation or quick overview

• PSD layer exporter
• Images to hypercube aggregation (*.npy supported)
• Information gain calculator
• 3D matrix cropping tool (*.mat and *.npy format supported)

Project organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── stable             <- tools which are handy and stable, move them here from src, write doc, tests and scritps
├── src                <- Experimental codes, anything you want to test and debug (or parametrize).
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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Project based on the cookiecutter data science project template. #cookiecutterdatascience